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This study aimed to develop a predictive model to screen for undetected vertical root fractures (VRFs) in root canal treated teeth. We included 95 root canal treated teeth with suspected VRFs; 77 for training and 18 for validation. Following clinical and cone‐beam CT parameters were recorded: sex, tooth type, coronal restoration, time interval from completion of endodontic treatment to definitive diagnosis (TI), type of bone loss (BL), apical extent of root filling (AR) and the ratio of root filling diameter to the actual diameter in the coronal (1/3TA) and middle (2/3TA) root thirds. A predictive model p = 1/(1 – e−x) was generated, where x = −7.433 + 1.977BL + 1.479 (2/3TA) + 1.102 AR; the sensitivity and specificity were 0.852 and 0.875 for training and 0.917 and 0.833 for validation. VRF teeth were more likely to have vertical bone loss and overfilled root canals. This model had a high diagnostic efficacy for VRFs. [ABSTRACT FROM AUTHOR]

Entheseal changes (EC) are frequent in older individuals, and age at death is the main variable explaining the distribution of EC stages in adult skeletal samples. While this is already well known, the underlying process has not been studied in biological anthropology to date. The aim of the article is to describe the relationship between age and stages of EC in detail for 30 appendicular entheses in a large sample of adult skeletons (N = 721) with known age at death and sex, using several statistical tools (such as Spearman's rank correlation coefficients, logistic regression models, adjusted standardized residuals, and generalized odds ratios). The analyses indicate a moderate to strong relationship between age at death and EC for all the appendicular attachment sites, but also striking differences between fibrous and fibrocartilaginous entheseal attachment sites. These differences are discussed in light of the available biomedical data. [ABSTRACT FROM AUTHOR]

We study bias‐reduced estimators of exponentially transformed parameters in general linear models (GLMs) and show how they can be used to obtain bias‐reduced conditional (or unconditional) odds ratios in matched case‐control studies. Two options are considered and compared: the explicit approach and the implicit approach. The implicit approach is based on the modified score function where bias‐reduced estimates are obtained by using iterative procedures to solve the modified score equations. The explicit approach is shown to be a one‐step approximation of this iterative procedure. To apply these approaches for the conditional analysis of matched case‐control studies, with potentially unmatched confounding and with several exposures, we utilize the relation between the conditional likelihood and the likelihood of the unconditional logit binomial GLM for matched pairs and Cox partial likelihood for matched sets with appropriately setup data. The properties of the estimators are evaluated by using a large Monte Carlo simulation study and an illustration of a real dataset is shown. Researchers reporting the results on the exponentiated scale should use bias‐reduced estimators since otherwise the effects can be under or overestimated, where the magnitude of the bias is especially large in studies with smaller sample sizes. [ABSTRACT FROM AUTHOR]

Objectives: To explore the potential value of ultrasound radiomics in differentiating between benign and malignant breast nodules by extracting the radiomic features of two‐dimensional (2D) grayscale ultrasound images and establishing a logistic regression model. Methods: The clinical and ultrasound data of 1000 female patients (500 pathologically benign patients, 500 pathologically malignant patients) who underwent breast ultrasound examinations at our hospital were retrospectively analyzed. The cases were randomly divided into training and validation sets at a ratio of 7:3. Once the region of interest (ROI) of the lesion was manually contoured, Spearman's rank correlation, least absolute shrinkage and selection operator (LASSO) regression, and the Boruta algorithm were adopted to determine optimal features and establish a logistic regression classification model. The performance of the model was assessed using the area under the receiver operating characteristic curve (AUC), and calibration and decision curves (DCA). Results: Eight ultrasound radiomic features were selected to establish the model. The AUC values of the model were 0.979 and 0.977 in the training and validation sets, respectively (P =.0029), indicating good discriminative ability in both datasets. Additionally, the calibration and DCA suggested that the model's calibration efficiency and clinical application value were both superior. Conclusions: The proposed logistic regression model based on 2D grayscale ultrasound images could facilitate differential diagnosis of benign and malignant breast nodules. The model, which was constructed using ultrasound radiomic features identified in this study, demonstrated good diagnostic performance and could be useful in helping clinicians formulate individualized treatment plans for patients. [ABSTRACT FROM AUTHOR]

When evaluating the effect of psychological treatments on a dichotomous outcome variable in a randomized controlled trial (RCT), covariate adjustment using logistic regression models is often applied. In the presence of covariates, average marginal effects (AMEs) are often preferred over odds ratios, as AMEs yield a clearer substantive and causal interpretation. However, standard error computation of AMEs neglects sampling-based uncertainty (i.e., covariate values are assumed to be fixed over repeated sampling), which leads to underestimation of AME standard errors in other generalized linear models (e.g., Poisson regression). In this paper, we present and compare approaches allowing for stochastic (i.e., randomly sampled) covariates in models for binary outcomes. In a simulation study, we investigated the quality of the AME and stochastic-covariate approaches focusing on statistical inference in finite samples. Our results indicate that the fixed-covariate approach provides reliable results only if there is no heterogeneity in interindividual treatment effects (i.e., presence of treatment-covariate interactions), while the stochastic-covariate approaches are preferable in all other simulated conditions. We provide an illustrative example from clinical psychology investigating the effect of a cognitive bias modification training on post-traumatic stress disorder while accounting for patients' anxiety using an RCT., (© 2024 The Author(s). British Journal of Mathematical and Statistical Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

Appropriate placement into courses at postsecondary institutions is critical for the success of students in terms of retention and graduation rates. To reduce the number of students who are misplaced, using multiple measures in placing students is encouraged. However, in practice most postsecondary schools utilize only a few measures to determine course placement. One of the reasons is the lack of research on methodologies that can be used to develop and establish appropriate cutoff scores using multiple measures. The purpose of this study is to investigate whether the classification tree model is a useful alternative approach to the multiple logistic regression model for placement into college courses. For comparison, this research examined the approaches' effectiveness and predictive accuracy for College Algebra. Data were obtained from two medium‐sized four year, midwestern institutions. Using two well‐known tree models, important measures and their cutoff scores for College Algebra were determined. The findings of this study showed that tree models were an effective alternative approach. Tree models also performed better than, or as well as, the multiple logistic regression model in predictive accuracy. [ABSTRACT FROM AUTHOR]

This study develops statistical and machine learning models based on discriminant analysis, logistic regression analysis, and a support vector machine (SVM) to predict the occurrence of radiation fog in Japan. The selection of a suitable set of explanatory variables for the models was made using the Akaike information criterion (AIC). The accuracies of the three models were measured and compared. To determine the optimum combination of explanatory variables, temperature, humidity, wind speed, precipitation, sunshine, and visibility data were considered. Based on the root mean square error (RMSE) and AIC values, the best combination of variables was found to include: the presence of precipitation, mean wind speed during the night, minimum temperature during the night, the amount of temperature cooling during the night, the minimum humidity during the previous day, and visibility at 18:00. A comparison of the predictive accuracies of the three models using the selected variable combination showed that the discriminant model produced a critical success index (CSI, or threat score) of 22.5, while the logistic regression model and the SVM model both produced better CSI results—with scores of 33.8 and 38.2, respectively. [ABSTRACT FROM AUTHOR]

The DMP‐criterion is proposed to generate optimal designs for the logistic regression model with reduced separation probabilities. This compound criterion has two components: (a) the D‐efficiency of the candidate design and (b) a penalty term that captures the average distance of the candidate design's support points from the region of maximum prediction variance (MPV). A DMP‐optimal design maximizes the DMP‐criterion. The aim is to obtain compromise experimental designs with high D‐efficiencies that are more robust to separation than a D‐optimal design of equal size. This paper presents the DMP‐criterion and demonstrates examples of its potential use as a means of mitigating separation in the design phase of a binary response experiment. For the examples presented, the local DMP‐optimal designs offer a 20‐30% reduction in separation probability over the local D‐optimal designs while maintaining D‐efficiencies over 93%. A robust design methodology is also demonstrated, where a robust DMP‐optimal design is compared to a Bayesian D‐optimal design and shown to have comparable D‐efficiencies across a range of randomly drawn parameter values while offering a mean reduction in separation probability of 23.9%. [ABSTRACT FROM AUTHOR]

We propose a combined model, which integrates the latent factor model and a sparse graphical model, for network data. It is noticed that neither a latent factor model nor a sparse graphical model alone may be sufficient to capture the structure of the data. The proposed model has a latent (i.e., factor analysis) model to represent the main trends (a.k.a., factors), and a sparse graphical component that captures the remaining ad‐hoc dependence. Model selection and parameter estimation are carried out simultaneously via a penalized likelihood approach. The convexity of the objective function allows us to develop an efficient algorithm, while the penalty terms push towards low‐dimensional latent components and a sparse graphical structure. The effectiveness of our model is demonstrated via simulation studies, and the model is also applied to four real datasets: Zachary's Karate club data, Kreb's U.S. political book dataset (http://www.orgnet.com), U.S. political blog dataset , and citation network of statisticians; showing meaningful performances in practical situations. [ABSTRACT FROM AUTHOR]

The novel coronavirus has spread quite rapidly across the globe. The current testing rate is failing to match the exponential rate of rising cases. Moreover, the available testing methodologies are expensive and time‐consuming. A sensitive automated diagnosis is one of the biggest need of the hour. In the proposed work, the authors analyse the chest X‐ray images of normal, pneumonia and coronavirus disease‐2019 (COVID‐19) patients and process them to boost the COVID‐specific features (opacities etc.), which enable to perform sensitive identification of COVID‐19 patients. The sets of original and processed images are used with a stack of pre‐trained deep models for ensemble learning. They used VGG‐16 as base‐learners, trained with a diverse set of inputs followed by a logistic regression model, the meta learner, to combine the base‐learner predictions. The proposed fusion‐based model is trained and tested for three types of classification, TYPE‐I: binary (NORMAL/ABNORMAL), TYPE‐II: binary (PNEUMONIA/COVID‐19) and TYPE‐III: multi‐class (NORMAL/PNEUMONIA/COVID‐19). The diagnosis results are quite promising, with high accuracy and sensitivity values for all the cases. The proposed algorithm can be used to assist the medical experts for quick identification and isolation of COVID‐19 patients and thereby mitigating the effect of the virus. [ABSTRACT FROM AUTHOR]

This study aims to offer multiple‐model informed predictions of COVID‐19 in Canada, specifically through the use of deep learning strategy and mathematical prediction models including long‐short term memory network, logistic regression model, Gaussian model, and susceptible‐infected‐removed model. Using the published data as of the 10th of April 2020, the authors forecast that the daily increased number of infective cases in Canada has not reached the peak turning point and will continue to increase. Therefore, Canada's healthcare services need to be ready for the magnitude of this pandemic. [ABSTRACT FROM AUTHOR]

Many stock investors make investment decisions based on stock-price-related chip indicators. However, in addition to quantified data, financial news often has a nonnegligible impact on stock price. Nowadays, as new reviews are posted daily on social media, there may be value in using web opinions to improve the performance of stock price prediction. To this end, we use logistic regression to screen the chip indicators and establish a basic stock price prediction model. Then, we employ text mining technology to quantify the unstructured data of social media opinions on stock-related news into sentiment scores, which are found to correlate significantly with the change extent of the stock price. Based on the findings that the higher the sentiment scores, the lower the prediction accuracy of the logistic regression model, we propose an improved prediction approach that integrates sentiment scores into the logistic regression model. Our results show that the proposed model can improve the prediction accuracy for stock prices, and can thus provide a new reference for investment strategies for stock investors. [ABSTRACT FROM AUTHOR]

In this study, the monofractal and multifractal properties of inter‐beat (R‐R) intervals in cardiac signals for normal and pathology classes were studied and applied to a dataset in PhysioNet which consists of 24 h R‐R intervals from 54 healthy subjects (hs) and 92 patients with various diagnoses [44 with congestive heart failure (chf), 25 with atrial fibrillation (af) and 23 diagnosed with sudden death syndrome (sd)]. The results in this study indicate that the most suitable method for estimating the monofractal properties of R‐R intervals is detrending moving average (DMA). The Hurst exponents (H) of the healthy and pathological groups, calculated using the DMA method, are shown to be statistically different by the Kruskal–Wallis test [p‐value < 0.01; healthy Hhs = 0.23(0.18–0.27); pathological group Hchf = 0.06(0.03–0.10), Haf = 0.05(0.03–0.07), Hsd = 0.05(0.03–0.08)]. To study the multifractal properties of R‐R intervals, the multifractal detrended fluctuation analysis was used. Formal statistical tests indicate statistically significant differences (p‐value < 0.05, ANOVA and pairwise testing) between the width of the spectrum of the chf group Wchf = 1.11 ± 0.12 and the width of the spectra of the af group Waf = 0.58 ± 0.15, the sd group Wsd = 0.60 ± 0.20, and the hs group Whs = 0.61 ± 0.08. Using these results, a logistic regression model was developed for differentiating chf from other pathologies (af and sd). [ABSTRACT FROM AUTHOR]

Inverse‐probability‐of‐treatment weighted (IPTW) estimation has been widely used to consistently estimate the causal parameters in marginal structural models, with time‐dependent confounding effects adjusted for. Just like other causal inference methods, the validity of IPTW estimation typically requires the crucial condition that all variables are precisely measured. However, this condition, is often violated in practice due to various reasons. It has been well documented that ignoring measurement error often leads to biased inference results. In this paper, we consider the IPTW estimation of the causal parameters in marginal structural models in the presence of error‐contaminated and time‐dependent confounders. We explore several methods to correct for the effects of measurement error on the estimation of causal parameters. Numerical studies are reported to assess the finite sample performance of the proposed methods. [ABSTRACT FROM AUTHOR]

Liver necroinflammation is the indicator for treating patients with chronic hepatitis B (CHB) infection. However, there is no suitable non‐invasive index for diagnosing liver necroinflammation. This study aimed to create a non‐invasive index to predict liver necroinflammation in patients who lack clear‐cut clinical inflammation parameters. Patients who were hepatitis B e antigen (HBeAg)‐negative and underwent liver histological diagnosis, had a normal or minimally increased alanine aminotransferase (ALT) level were enrolled. Liver necroinflammation was defined as histological active index ≥4. A logistic regression model (LRM) was established based on the parameters independently associated with liver necroinflammation. Of all 550 patients, 36.73% had necroinflammation. In patients with an abnormal ALT level, the rate of necroinflammation was 52.49%. The area under the curve (AUC) of the ALT level for predicting necroinflammation was 0.655 (95% confidence interval [CI], 0.609‐0.702), and that of the HBV DNA level ≥2000 IU/mL combined with an abnormal ALT level was 0.618. By using the LRM, the AUC improved to 0.769 (95% CI, 0.723‐0.815) with a Youden index of 0.519 and diagnostic accuracy of 75.3%. The cutoff value ≥0.7 in the LRM had a specificity of 97.4% and positive predictive value of 85.0% for predicting necroinflammation. By using the cutoff value <0.15 in the LRM, the presence of necroinflammation could be excluded with a negative predictive value of 90.8%. This study indicated that the LRM can be used to effectively diagnose liver necroinflammation in HBeAg‐negative patients with CHB who have normal or minimally elevated ALT levels. [ABSTRACT FROM AUTHOR]

The D‐optimality criterion is often used in computer‐generated experimental designs when the response of interest is binary, such as when the attribute of interest can be categorized as pass or fail. The majority of methods in the generation of D‐optimal designs focus on logistic regression as the base model for relating a set of experimental factors with the binary response. Despite the advances in computational algorithms for calculating D‐optimal designs for the logistic regression model, very few have acknowledged the problem of separation, a phenomenon where the responses are perfectly separable by a hyperplane in the design space. Separation causes one or more parameters of the logistic regression model to be inestimable via maximum likelihood estimation. The objective of this paper is to investigate the tendency of computer‐generated, nonsequential D‐optimal designs to yield separation in small‐sample experimental data. Sets of local D‐optimal and Bayesian D‐optimal designs with different run (sample) sizes are generated for several "ground truth" logistic regression models. A Monte Carlo simulation methodology is then used to estimate the probability of separation for each design. Results of the simulation study confirm that separation occurs frequently in small‐sample data and that separation is more likely to occur when the ground truth model has interaction and quadratic terms. Finally, the paper illustrates that different designs with identical run sizes created from the same model can have significantly different chances of encountering separation. [ABSTRACT FROM AUTHOR]

Dynamic security assessment is widely used in dispatching operation systems, and calculation speed is one of its most important performance indices. In this study, an improved k-nearest neighbour (k-NN) method is proposed aiming to predict the stability indicators of power system, for example, critical clearing time. The method is much faster than the simulation and suitable for online analysis. Firstly, a simulation sample database is constructed based on historical online data and a logistic regression model with least absolute shrinkage and selection operator is trained to pick the stability features, which are chosen from static quantities like running state and active power of electric elements. While a new operation mode needs to be evaluated, a weighted k-NN is implemented to obtain the most familiar samples in the database using the chosen features; the final result will be determined comprehensively by the familiar samples. The validity of the proposed method is verified by simulation using online data of State Grid Corp of China and different key faults. It is proved that the method meets the requirements for speed and accuracy of online analysis system. [ABSTRACT FROM AUTHOR]

Abstract: It is well known that pigs are sensitive to heat stress, but few studies have assessed the critical temperature that affects farrowing rate. Therefore, the objective of the present study was to assess the effects of outside temperature on farrowing rate by using a multivariate logistic regression model. Data were obtained from 25 commercial farms, including 26,128 service records for gilts and 120,655 service records for sows. Two variables, maximum temperature (MAX) and temperature humidity index (THI), were used as an indicator for climate conditions. In gilts, an interaction between climate conditions and service number was associated with farrowing rate (p < .05). In the first service, farrowing rate decreased as climate conditions increased, whereas no relationship was found in the second service or later. In sows, farrowing rate at first service decreased as MAX increased from 22°C or THI increased from 66 (p < .05), but no apparent reduction under heat conditions was found in the second service or later. Additionally, effect of heat stress on farrowing rate in parities 1–2 was higher than those in parities 3–5 and ≥6 (p < .05). These results can be applied to field conditions as a standard for the critical temperature for farrowing rate. [ABSTRACT FROM AUTHOR]

Abstract: There is a great deal of recent interests in modeling right‐censored clustered survival time data with a possible fraction of cured subjects who are nonsusceptible to the event of interest using marginal mixture cure models. In this paper, we consider a semiparametric marginal mixture cure model for such data and propose to extend an existing generalized estimating equation approach by a new unbiased estimating equation for the regression parameters in the latency part of the model. The large sample properties of the regression effect estimators in both incidence and the latency parts are established. The finite sample properties of the estimators are studied in simulation studies. The proposed method is illustrated with a bone marrow transplantation data and a tonsil cancer data. [ABSTRACT FROM AUTHOR]

Using direct loan data for 2012 to 2014 from the Korea Student Aid Foundation, we develop a risk group predictive model for borrowers defaulting on their loans. We used a logistic regression model and the Cox proportional hazards model to develop the risk predictive model. We verified the validity of the models using a receiver operating characteristic curve and a validation dataset. The present study shows that area under the receiver operating characteristic curves is similar for the models and that the major influencing factors for defaulting on their loans are household income, whether a national grant was received, age, whether more than two accounts are overdue, field of study and the monthly repayment amount. The risk group predictive model in this study will be the basis for more efficient management of direct student loans. [ABSTRACT FROM AUTHOR]

Objective: To establish a logistic regression model using surface electromyography (SEMG) parameters for diagnosing the compressed nerve root at L5 or S1 level in patients with lumbar disc herniation (LDH). Methods: This study recruited 24 patients with L5 nerve root compression and 23 patients with S1 nerve root compression caused by LDH from May 2014 to May 2016. SEMG signals from the bilateral tibialis anterior and lateral gastrocnemius were measured. The root mean square (RMS), the RMS peak time, the mean power frequency (MPF), and the median frequency (MF) were analyzed. The accuracy, sensitivity, and specificity values were calculated separately. The areas under the curve (AUC) of the receiver‐operating characteristic (ROC) curve and the kappa value were used to evaluate the accuracy of the SEMG diagnostic model. Results: The accuracy of the SEMG model ranged from 85.71% to 100%, with an average of 93.57%. The sensitivity, specificity, AUC, and kappa value of the logistic regression model were 0.98 ± 0.05, 0.92 ± 0.09, 0.95 ± 0.04 (P = 0.006), and 0.87 ± 0.11, respectively (P = 0.001). The final diagnostic model was: P = 1 − 1 1 + e y; y = 10.76 − (5.95 × TA_RMS Ratio) − (0.38 × TA_RMS Peak Time Ratio) – (5.44 × 44 × LG_RMS Peak Time Ratio). L5 nerve root compression is diagnosed when P < 0.5 and S1 nerve root compression when P ≥ 0.5. Conclusions: The logistic regression model developed in this study showed high diagnostic accuracy in detecting the compressed nerve root (L5 and S1) in these patients with LDH. [ABSTRACT FROM AUTHOR]

There remains continued use of non-linear, logistic regression models for predicting water temperature from air temperature. A dominant feature of these non-linear models is an upper bound on river water temperature. This upper bound is often attributed to a large increase in evaporative cooling at high air temperatures, but the exact conditions under which such an increase may occur have not been thoroughly explored. To better understand the appropriateness of the non-linear model for predicting river water temperatures, it is essential to understand the physical basis for the upper bound and when it should and should not be included in the statistical model. This paper applies and validates an energy balance model against 8 river systems spread across different climate regions of the United States. The energy balance model is then used to develop a diagram relating vapour pressure deficit and air temperature to water temperature. With knowledge of present or future vapour pressure deficit (difference between saturation and actual vapour content in the atmosphere) conditions in a given climate, the diagram can be used to predict the likelihood of an upper bound in the air-water temperature relationship. This investigation offers a fundamental physical explanation of the most appropriate form of statistical models that should be used for predicting future water temperature from air temperature in different geographic regions with different climate conditions. In general, climatic regions that have only a slight increase in vapour pressure deficit with increasing air temperature (typically humid regions) would not be expected to have an upper bound. Conversely, climatic regions in which vapour pressure deficit sharply increases with increasing air temperature (typically arid regions) would be expected to have an upper bound. [ABSTRACT FROM AUTHOR]

Objective: To establish a statistical model to predict thrombophilia in patients with unexplained recurrent pregnancy loss (URPL).Methods: A retrospective case-control study was conducted at Ren Ji Hospital, Shanghai, China, from March 2014 to October 2016. The levels of D-dimer (DD), fibrinogen degradation products (FDP), activated partial thromboplastin time (APTT), prothrombin time (PT), thrombin time (TT), fibrinogen (Fg), and platelet aggregation in response to arachidonic acid (AA) and adenosine diphosphate (ADP) were collected. Receiver operating characteristic curve analysis was used to analyze data from 158 UPRL patients (≥3 previous first trimester pregnancy losses with unexplained etiology) and 131 non-RPL patients (no history of recurrent pregnancy loss). A logistic regression model (LRM) was built and the model was externally validated in another group of patients.Results: The LRM included AA, DD, FDP, TT, APTT, and PT. The overall accuracy of the LRM was 80.9%, with sensitivity and specificity of 78.5% and 78.3%, respectively. The diagnostic threshold of the possibility of the LRM was 0.6492, with a sensitivity of 78.5% and a specificity of 78.3%. Subsequently, the LRM was validated with an overall accuracy of 83.6%.Conclusion: The LRM is a valuable model for prediction of thrombophilia in URPL patients. [ABSTRACT FROM AUTHOR]

ABSTRACT East China ( EC) has experienced a number of severe droughts in recent decades. Understanding the characteristics of droughts and their persistence will provide operational guidelines for water resource management and agricultural production. This study uses a logistic regression model to measure the probability of drought occurrence in the current season given the previous season's Standardized Precipitation Index ( SPI) and Southern Oscillation Index ( SOI) as well as drought persistence. Results reveal large spatial and seasonal variations in the relationship between the previous season's SPI and the drought occurrence probability in a given season. The drought persistence averaged over the entire study area for all the four seasons is approximately 34% with large variations from season to season and from region to region. The East and Northeast regions have the largest summer drought persistence (∼40%) and lowest fall drought persistence (∼28%). The spatial pattern in winter and spring drought persistence is dissimilar with stronger winter and weaker spring drought persistence in the Southwest and Northeast relative to other regions. Logistic regression analysis indicates a stronger negative relationship in summer-to-fall (or between fall drought occurrence and summer SPI) than other inter-season relationships. Most of the stations with significant relationships in summer-to-fall lie in the East and the southern part of the North region. Significant negative relationships in winter-spring and fall-winter are primarily located on the western portion of the study area. A significant negative spring-summer relationship exists in the North region. Further, spring drought occurrence has a statistically significant positive relationship with winter SOI in the Southeast. Fall drought occurrence and summer SOI are negatively correlated in the northwest portion of EC (i.e. the border of North and Southwest regions). Overall, this study demonstrates that the impact of previous season SPI and SOI on current season drought varies substantially from region to region and from season to season. This study also shows stronger drought persistence in summer than in other seasons. In other words, the probability of fall drought occurrence is closely related to summer moisture conditions in the EC. [ABSTRACT FROM AUTHOR]

Prior research indicates that 10-15 cases or controls, whichever fewer, are required per parameter to reliably estimate regression coefficients in multivariable logistic regression models. This condition may be difficult to meet even in a well-designed study when the number of potential confounders is large, the outcome is rare, and/or interactions are of interest. Various propensity score approaches have been implemented when the exposure is binary. Recent work on shrinkage approaches like lasso were motivated by the critical need to develop methods for the p >> n situation, where p is the number of parameters and n is the sample size. Those methods, however, have been less frequently used when p≈n, and in this situation, there is no guidance on choosing among regular logistic regression models, propensity score methods, and shrinkage approaches. To fill this gap, we conducted extensive simulations mimicking our motivating clinical data, estimating vaccine effectiveness for preventing influenza hospitalizations in the 2011-2012 influenza season. Ridge regression and penalized logistic regression models that penalize all but the coefficient of the exposure may be considered in these types of studies. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background The purpose of this study was to investigate factors associated with osteoradionecrosis (ORN) of the mandible bone in a large cohort of patients with oral cancer. Methods We reviewed the medical records of patients with oral cancer and identified those with ORN of the mandible bone. Variables of patients with and without ORN were compared and associated factors were investigated by logistic regression model. Results A total of 1692 patients were included in the final analysis and 105 patients (6.2%) developed ORN in the mandible bone. Primary site, including mouth floor, buccal mucosa, retromolar trigone, or gum, segmental mandibulectomy, and total radiation dose to the primary site ≥75 Gy were independent factors associated with ORN. After aggressive treatment using surgical intervention with/without hyperbaric oxygen, 93.3% of the patients healed completely. Conclusion Among patients with oral cancer after radiation, ORN is an uncommon and dreaded complication. Recognition of associated factors can help physicians to identify those at risk. © 2015 Wiley Periodicals, Inc. Head Neck 38: 762-768, 2016 [ABSTRACT FROM AUTHOR]

Objective To investigate the incidence of severe maternal morbidities ( SMM) in China and explore effects of medical co-morbidities on SMM. Design Proactive multicenter clinic register collaboration. Setting Data on all deliveries at eight hospitals in Sichuan province, China, collected from 1 January 2009 to 12 December 2010. Population 33 993 delivering women and 34 547 live births. Methods We defined SMM as a combination indicator of severe maternal complications, critical interventions, admission to the intensive care unit and maternal near-miss instances. We randomly selected 80% of the data from the entire database to build a logistic regression model. The remaining 20% were used to test the predictive power of the model. Main outcome measures SMM incidence, adjusted odds ratios ( aORs), and area under a receiver operating characteristic ( ROC) curve. Results Severe maternal morbidities incidence was 43.4/1000 live births [confidence interval ( CI) 41.24-45.56]. Fifteen variables were independent contributors to the model. Seven medical co-morbidities significantly affected the occurrence of SMM, including iron-deficiency anemia ( aOR 3.07, CI 2.47-3.83) and other hematological diseases ( aOR 5.82, CI 3.50-9.69), hepatitis-B virus infection ( aOR 1.48, CI 1.12-1.97) and other hepatic diseases ( aOR 3.81, CI 1.61-9.04), cardiopathy ( aOR 3.59, CI 2.62-4.93), hypertension ( aOR 5.23, CI 4.06-6.75), and respiratory diseases ( aOR 2.10, CI 1.25-3.52). The area under the ROC curve was 0.8127. Conclusions The incidence of SMM was typical of a low resource area. There is a need to identify medical co-morbidities and to adopt prophylactic measures and interventions. [ABSTRACT FROM AUTHOR]

Headwater streams are the primary sources of water in a drainage network and serve as a critical hydrologic link between the surrounding landscape and larger, downstream surface waters. Many states, including North Carolina, regulate activity in and near headwater streams for the protection of water quality and aquatic resources. A fundamental tool for regulatory management is an accurate representation of streams on a map. Limited resources preclude field mapping every headwater stream and its origin across a large region. It is more practical to develop a model for headwater streams based on a sample of field data that can then be extrapolated to a larger area of interest. The North Carolina Division of Water Quality has developed a cost-effective method for modeling and mapping the location, length, and flow classification (intermittent and perennial) of headwater streams. We used a multiple logistic regression approach that combined field data and terrain derivatives for watersheds located in the Triassic Basins ecoregion. Field data were collected using a standard methodology for identifying headwater streams and origins. Terrain derivatives were generated from digital elevation models interpolated from bare-earth Light Detection and Range data. Model accuracies greater than 80% were achieved in classifying stream presence and absence, stream length and perennial stream length, but were not as consistent in predicting intermittent stream length. [ABSTRACT FROM AUTHOR]

Background The purpose of this study was to investigate factors associated with carotid blowout in the cervical portion of patients with head and neck cancer in a large cohort. Methods We reviewed patients with head and neck cancer and identified those with carotid blowout. Variables of patients with and without carotid blowout were compared and associated factors were investigated by logistic regression model. Results A total of 2590 patients were included in the final analysis and 102 patients (3.9%) developed carotid blowout in the cervical region. Body mass index (BMI) <22.5 kg/M2, primary site in the hypopharynx or oropharynx, open wound in the neck requiring wet dressing, radical neck dissection, and total radiation dose to the neck ≥70 Gy were independent factors associated with carotid blowout. Conclusion After completion of treatment in patients with head and neck cancer, carotid blowout was an uncommon complication with catastrophic results. Recognition of associated factors helps physicians to identify those at risk. © 2014 Wiley Periodicals, Inc. Head Neck 37: 265-272, 2015 [ABSTRACT FROM AUTHOR]

To estimate potential impact of climate change on wheat fusarium ear blight ( FEB), simulated weather for the A1B climate change scenario was input into a model for estimating FEB in central China. In this article, a logistic weather-based regression model for estimating incidence of wheat FEB in central China was developed, using up to 10 years (2001-2010) of disease, anthesis date and weather data available for 10 locations in Anhui and Hubei provinces. In the model, the weather variables were defined with respect to the anthesis date for each location in each year. The model suggested that incidence of FEB is related to number of days of rainfall in a 30-day period after anthesis and that high temperatures before anthesis increase the incidence of disease. Validation was done to test whether this relationship was satisfied for another five locations in Anhui province with FEB data for 4-5 years but no nearby weather data, using simulated weather data obtained employing the regional climate modelling system PRECIS. How climate change may affect wheat anthesis date and FEB in central China was investigated for period 2020-2050 using wheat growth model Sirius and climate data simulated using PRECIS. The projection suggested that wheat anthesis dates will generally be earlier and FEB incidence will increase substantially for most locations. [ABSTRACT FROM AUTHOR]

To represent the high concentration of recovery rates at the boundaries, we propose to consider the recovery rate as a mixed random variable, obtained as the mixture of a Bernoulli random variable and a beta random variable. We suggest to estimate the mixture weights and the Bernoulli parameter by two logistic regression models. For the recovery rates belonging to the interval (0,1), we model, jointly, the mean and the dispersion by using two link functions, so we propose the joint beta regression model that accommodates skewness and heteroscedastic errors. This methodological proposal is applied to a comprehensive survey on loan recovery process of Italian banks. In the regression model, we include some macroeconomic variables because they are relevant to explain the recovery rate and allow to estimate it in downturn conditions, as Basel II requires. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT The aim of this study was to investigate the main factors that influence smallholders' adoption decision of soil conservation measures in the Gedeb watershed. Data from 498 household heads who live in the three districts of the watershed were analysed using the binary logistic regression model. We find that farmers need adequate cash to invest in soil conservation measures. Moreover, farmers would be more encouraged to implement soil conservation measures when they have larger areas of cropland. We explore the possibility that when farmers presume that they have fertile land, they exploit their land more. This hints at the need for extension advice about the benefit of sustainable use of farmers' cropland so that they can maintain their land and pass it on to the future generation. Farmers' awareness about the benefit of land management practices and recognition of the problem erosion is causing on their crop land are central to their decision to adopt soil conservation measures. Furthermore, to adopt these measures, farmers have to be convinced about the effectiveness of these measures. Thus, awareness creation and demonstration of the effectiveness of these measures is essential. Because of the transboundary nature of the problem, policy makers in downstream countries that are suffering from the off-site impact (e.g. Sudan) would benefit from the information provided and support efforts in the implementation of soil conservation measures. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In randomized clinical trials, it is common that patients may stop taking their assigned treatments and then switch to a standard treatment (standard of care available to the patient) but not the treatments under investigation. Although the availability of limited retrieved data on patients who switch to standard treatment, called off-protocol data, could be highly valuable in assessing the associated treatment effect with the experimental therapy, it leads to a complex data structure requiring the development of models that link the information of per-protocol data with the off-protocol data. In this paper, we develop a novel Bayesian method to jointly model longitudinal treatment measurements under various dropout scenarios. Specifically, we propose a multivariate normal mixed-effects model for repeated measurements from the assigned treatments and the standard treatment, a multivariate logistic regression model for those stopping the assigned treatments, logistic regression models for those starting a standard treatment off protocol, and a conditional multivariate logistic regression model for completely withdrawing from the study. We assume that withdrawing from the study is non-ignorable, but intermittent missingness is assumed to be at random. We examine various properties of the proposed model. We develop an efficient Markov chain Monte Carlo sampling algorithm. We analyze in detail via the proposed method a real dataset from a clinical trial. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective To generate and evaluate a new logistic regression model for the prediction of successful expectant management of first trimester miscarriage. Methods Data were collected prospectively from women diagnosed with 1st trimester miscarriage. Clinical and ultrasonographic variables were recorded for multivariate analysis. Clinically stable women who were managed expectantly were followed up for two weeks until the outcome was established: success or failure. A multinomial logistic regression ( MLR) model was developed on 186 training cases for the prediction of successful expectant management and tested prospectively on a further 126 cases. The performance of the model was evaluated using receiver operating characteristic ( ROC) curve as well as in terms of sensitivity, specificity, positive predictive value ( PPV) and negative predictive value ( NPV). Results Two thousand and forty eight consecutive first trimester women underwent TVS. Complete data from 312 (15.2%) women with miscarriage managed expectantly were included in the final analysis. The most important independent prognostic variables for the MLR model were as follows: type of miscarriage at primary scan, vaginal bleeding and maternal age. When developed retrospectively on a training data set, MLR model gave an area under the ROC curve ( AUC) of 0.796. Prospective validation of MLR model on a new test data set resulted in an AUC of 0.803. Conclusion We have developed and validated a new mathematical model to predict successful management of first trimester miscarriage. [ABSTRACT FROM AUTHOR]

Objective The aim was to develop a new model to predict the outcome at the end of the 1st trimester after a single visit to the early pregnancy unit ( EPU). Methods Prospective observational study in the EPU at Nepean Hospital, between November 2006 and February 2009. Data were collected from all women in the 1st trimester of their pregnancy who had a live intrauterine pregnancy ( IUP) at the 1st transvaginal ultrasound scan ( TVS). 29 historical, clinical and ultrasound end points were recorded. Women were followed until the final diagnosis was established at the end of the 1st trimester: viability or nonviability. A multinomial logistic regression model was developed. The performance of this model was evaluated using receiver operating characteristic ( ROC) curves. Results Data from 416 pregnancies were included: 92.1% were live beyond the 1st trimester, and 7.9% had miscarried. The most useful prognostic variables for developing the logistic regression model were gestational age by dates, vaginal ( PV) bleeding, PV clots, gestational age by TVS, consistency with menstrual dates, mean gestational sac ( GS) size, mean yolk sac ( YS) size and number of previous caesarean sections. Used retrospectively on 416 women based on 25 imputations, the model gave an AUC of 0.88. Based on cross-validation, the independent predictive power obtained an AUC of 0.78. Conclusions We have developed a new model to predict the outcome of the 1st trimester in women with live IUP at the 1st scan. [ABSTRACT FROM AUTHOR]

In medical diagnostic testing problems, the covariate adjusted receiver operating characteristic (ROC) curves have been discussed recently for achieving the best separation between disease and control. Due to various restrictions such as cost, the availability of patients, and ethical issues quite frequently only limited information is available. As a result, we are unlikely to have a large enough overall sample size to support reliable direct estimations of ROCs for all the underlying covariates of interest. For example, some genetic factors are less commonly observable compared with others. To get an accurate covariate adjusted ROC estimation, novel statistical methods are needed to effectively utilize the limited information. Therefore, it is desirable to use indirect estimates that borrow strength by employing values of the variables of interest from neighbouring covariates. In this paper we discuss two semiparametric exponential tilting models, where the density functions from different covariate levels share a common baseline density, and the parameters in the exponential tilting component reflect the difference among the covariates. With the proposed models, the estimated covariate adjusted ROC is much smoother and more efficient than the nonparametric counterpart without borrowing information from neighbouring covariates. A simulation study and a real data application are reported. The Canadian Journal of Statistics 40: 569-587; 2012 © 2012 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

The factors affecting mortality in Diplodus annularis (L.) and Lithognathus mormyrus (L.) caught and released in a recreational fishery in the Balearic Islands (NW Mediterranean) were examined. A total of 194 and 214 specimens of each species, respectively, were caught and individually monitored in containment tanks for 10 days. The circumstances of the catch, such as capture depth, water temperature, fish size, hook type, hook location, bleeding, unhooking time and cutting the hook line were tested with a logistic regression model. Diplodus annularis experienced moderate rates of mortality (15%), and only deep-hooking was a significant predictor of death. The incidence of undersized fish for this species was 48%. By contrast, L. mormyrus exhibited higher mortality rates (33%) with over 90% of the catch under the minimum legal size. Deep-hooking was the strongest predictor of mortality. When circle hooks were used, or if the line was cut when the fish were deep-hooked, mortality was considerably reduced. Strategies, such as promoting the use of more selective gears that reduce the capture of undersized fish and implementing gentler release techniques, should be considered in managing these species. [ABSTRACT FROM AUTHOR]

Aims. To describe registered nurses’ reported use of research in the care of older people and to examine associations between research use and factors related to the elements: the communication channels, the adopter and the social system. Background. Research use among registered nurses working in hospital settings has been reported in many studies. Few studies, however, have explored the use of research among registered nurses working in the care of older people. Design. A cross-sectional survey. Methods. In eight municipalities, all registered nurses ( n = 210) working in older people care were invited to participate (response rate 67%). The Research Utilisation Questionnaire was adopted. Questions concerning the work organisation and research-related resources were sent to the Community Chief Nurse at each municipality. Descriptive statistics and logistic regression were applied. Results. The registered nurses reported a relatively low use of research findings in daily practice, despite reporting a positive attitude to research. The registered nurses reported lack of access to research reports at the work place and that they had little support from unit managers and colleagues. Registered nurses working in municipalities with access to research-related resources reported more use of research than registered nurses without resources. The factors ‘Access to research findings at work place’, ‘Positive attitudes to research’ and ‘Nursing programme at university level’ were significantly associated with research use. Conclusions. There is a great potential to increase registered nurses’ use of research findings in the care of older people. Factors which were linked to the communication channels and the adopter were associated with research use. Relevance to clinical practice. Strategies to enhance research use should focus on access to and adequate training in using information sources, increased knowledge on research methodology and nursing science and a supportive organisation. [ABSTRACT FROM AUTHOR]

Climate change is expected to drive species ranges towards the poles and to have a strong influence on species distributions. In this study, we focused on diadromous species that are of economical and ecological importance in the whole of Europe. We investigated the potential distribution of all diadromous fish regularly encountered in Europe, North Africa and the Middle East (28 species) under conditions predicted for twenty-first century climate change. To do so, we investigated the 1900 distribution of each species in 196 basins spread across all of Europe, North Africa and the Middle East. Four levels were used to semiquantitatively describe the abundance of species, that is missing, rare, common and abundant. We then selected five variables describing the prevailing climate in the basins, the physical nature of the basins and reflecting historical events known to have affected freshwater fish distribution. Logistic regressions with a four-level ordinal response variable were used to develop species-specific models. These predictive models related the observed distribution of these species in 1900 to the most explanatory combination of variables. Finally, we selected the A2 SRES scenario and the HadCM3 (Hadley Centre Coupled Model version 3) global climate model (GCM) to obtain climate variables (temperature and precipitation) at the end of this century. We used these 2100 variables in our models and obtained maps of climatically suitable and unsuitable basins, percentages of contraction or expansion for each species. Twenty-two models were successfully built, that is there were five species for which no model could be established because their distribution range was too narrow and the Acipenser sturio model failed during calibration. All the models selected temperature or/and precipitation as explanatory variables. Responses to climate change were species-specific but could be classified into three categories: little or no change in the distribution (five species), expansion of the distribution range (three species gaining suitable basins mainly northward) and contraction of the distribution (14 species losing suitable basins). Shifting ranges were in accordance with those found in other studies and underlined the high sensitivity of diadromous fish to modifications in their environment. [ABSTRACT FROM AUTHOR]

Evidence-based guidelines recommend participation in cardiac rehabilitation (CR) to reduce subsequent cardiovascular events following percutaneous coronary intervention (PCI). The objective of this study was to investigate the reasons for and the demographic characteristics of patients who participate in CR programmes following PCI in a single metropolitan setting in Australia. Logistic regression models were used to examine the data obtained using self-reports through mailed questionnaires from 202 patients 12–24 months following the index PCI. Thirty-five per cent of subjects reported to have participated in CR programmes, of whom less than a quarter were female. In this sample the main reason for programme participation was a recommendation given by the general practitioner (GP). The likelihood of participating in a CR programme decreased with age and absence of a partner. Additional research on determinants of participation in CR programmes among this cohort of patients could assist in the development of new strategies to encourage healthy behaviours and reduce the risk of further cardiac events. [ABSTRACT FROM AUTHOR]

Aim To present a method that assesses the influence of environmental variables, including climate, substrate, topography, and anthropogenic disturbances, on the distribution of Andean forest at the tree line, and to compare this forest distribution between areas. Location Sangay National Park, on the eastern slopes of the Andes in central Ecuador. Methods A logistic regression model was built using topographical variables and environmental indices, derived from a digital elevation model, to explain forest cover, derived from a Landsat ETM image, in a zone around the average tree line altitude. Results The model shows that after altitude, which can explain about 80% of forest cover, wetness has the next strongest effect (areas accumulating water, but also cold air, were devoid of forest, resulting in inverted tree lines), followed by eastness (western slopes had forest to higher altitudes). Application of the model in two nearby areas showed that the real tree line was lower than the predicted tree line in both areas, probably owing mainly to macroclimatic differences in one area, and partly also to human land use in the other. The locations with the largest deviations could be the focus of further research concerning human impacts on tree line vegetation. Main conclusions The tree line is located at lower altitudes on east-facing slopes, which may be because high levels of radiation are received by east-facing slopes in the clear mornings, resulting in the photoinhibition of tree seedlings in the páramo. In spite of the limitations of the quality and resolution of the remote sensing data, the presented method provides indications for important ecological factors at the tree line. The method also allows the detection of differences in tree line position between areas, which may reflect climatic differences or the location of anthropogenic disturbances. [ABSTRACT FROM AUTHOR]

Objectives: This paper applies statistical methods to identify factors associated with pregnancy-related complications in rural Bangladesh. The results are examined in order to identify the factors that can help formulate policy for reducing pregnancy-related morbidity. Methods: Two statistical methods are used in this study to analyse the repeated-measures data on pregnancy-related complications in rural Bangladesh. One method is based on the application of linear logistic regression at each follow up, and the results are compared to identify the potential risk factors. Then the generalised estimating equation (GEE) approach is used, and the results based on each follow up are compared with those obtained from the application of the GEE approach. The maternal morbidity data collected by the Bangladesh Institute of Research for Promotion of Essential and Reproductive Health and Technologies are used in this paper. Results: Economic status, visit for antenatal care, and histories of sexually transmitted disease, hypertension, heart disease and breathing problem are positively associated with the complications during pregnancy, while wanted pregnancy, household type, primary and secondary schooling, and five or more pregnancies are associated negatively with the response variable. Involvement with gainful employment shows a negative association by the first technique. The GEE approach failed to identify some important factors such as history of anaemia, previous pregnancy and gainful employment. However, duration of pregnancy appears to have an association with pregnancy complications, which was not found from the conditional model based on each follow up. Conclusion: It appears that unwanted pregnancies need to be reduced through a well-organised and targeted family planning campaign for potential mothers of all ages in rural Bangladesh. Nutritional anaemia may cause various complications, and suggestions are given for reducing anaemia among pregnant women. In the rural areas of Bangladesh, first births usually occur at teenage. The first-birth pregnancy among teenagers can be a source of severe complications. There can be special programs to address the need for delay in the first pregnancy of those who marry at an early age. [ABSTRACT FROM AUTHOR]

Spatial heterogeneity of vegetation types and the abiotic environment can influence the occurrence and spread of wildfires, but in some landscapes the importance of these effects varies under conditions of severe fire weather. In the northern Patagonian landscape of forests and shrublands we examined the effects of vegetation type (tall forest vs. tall shrubland) and abiotic factors (elevation, topography, and precipitation) on fire occurrence at a broad scale and on fire spread at a fine scale. We used satellite images (1985-1999) and aerial photography (1950-1999) to map fires in relation to pre-burn vegetation type and abiotic factors. Fire extent is greatest at intermediate elevations and locations of intermediate precipitation. Fire extent is limited by lack of fuel quantity at the lower end of the precipitation gradient and by infrequent or insufficient fuel desiccation at the upper end. Tall shrublands are proportionally more affected by fire than are adjacent mesic forests of Nothofagus dombeyi and N. pumilio. Patches of subalpine forests often tend to serve as natural fire breaks, except under the most severe fire weather. Tall shrublands are dominated by species that resprout vigorously so that fuels quickly recover. In contrast, forests are dominated by species dependent on seed reproduction that sometimes fails after severe fires so that shrublands tend to replace burned forests. The greater propensity of shrublands to burn is a positive feedback that is favorable to fire and that accelerates the replacement of forest by shrublands. Infrequently occurring severe weather is important in the burning of otherwise relatively fire-resistant subalpine forests. Past burning, associated both with drought and early forest clearing, expanded shrublands at the expense of forests so that in the modern landscape an increase in anthropogenic ignitions and the positive feedback of fire and shrublands are synergistically accelerating conversion from forest to shrubland. [ABSTRACT FROM AUTHOR]

Question: Which factors affect the spatial variation of maquis and phryganic vegetation and how can the role of human activities be estimated through degradation models. Location: Island of Sifnos, Cyclades, Greece. Methods: Data on woody vegetation, environmental conditions and intensity of human activity were collected by field survey. The vegetation data were analysed using TWINSPAN. Models based on the combination of Canonical Variate Analysis and logistic regressions were used in order to examine and describe the different pathways of maquis degradation. Results: The spatial variation of maquis and phryganic vegetation was related to both environmental and disturbance factors. The replacement of maquis by phryganic vegetation can follow two different pathways which reflect differences on the environmental conditions. However, the pathways themselves are determined by the combinations and levels of disturbance only. Conclusion: Degradation models can provide essential information on the effects of human activities on the vegetation. Maquis vegetation is relatively tolerant of disturbance, however, if disturbance exceeds certain critical thresholds, maquis is replaced by phryganic vegetation. The presence of phryganic vegetation is not always the result of maquis degradation. [ABSTRACT FROM AUTHOR]

An empirical screening level approach was developed to assess the probability of toxicity to benthic organisms associated with contaminated sediment exposure. The study was based on simple logistic regression models (LRMs) of matching sediment chemistry and toxicity data retrieved from a large database of field-collected sediment samples contaminated with multiple chemicals. Three decisions were made to simplify the application of LRMs to sediment samples contaminated with multiple chemicals. First, percent mortality information associated with each sediment sample was condensed into a dichotomous response (i.e., toxic or nontoxic). Second, each LRM assumed that toxicity was attributable to a single contaminant. Third, individual contaminants present at low concentrations were excluded from toxic sediment samples. Based on an analysis of the National Sediment Inventory database, the LRM approach classified 55% of nontoxic sediments as toxic (i.e., false-positives). Because this approach has been used to assess the probability of benthic toxicity as reported by the U.S. Environmental Protection Agency (U.S. EPA), the resultant estimates of potential toxicity convey a misleading impression of the increased hazard that sediments pose to the health of aquatic organisms at many sites in the United States. This could result in important resources needlessly being diverted from truly contaminated sites to evaluate and possibly remediate sediments at uncontaminated sites. [ABSTRACT FROM AUTHOR]

Two models, a Epolycyclic aromatic hydrocarbon (PAH) model based on equilibrium partitioning theory and a logistic-regression model, weredeveloped and evaluated to predict sediment-associated PAH toxicity to Hyalella azteca. A EPAH model was applied to freshwater sediments.This study is the first attempt to use a EPAH model based on water-only, median lethal concentration (LC50) toxic unit (TU) values for sediment-associated PAH mixtures and its application to freshwater sediments. To predict the toxicity (i.e., mortality) from contaminated sediments to H. azteca, an interstitial water TU, calculated as the ambient interstitial water concentration divided by the water-only LC50 in which the interstitial water concentrations were predicted by equilibrium partitioning theory, was used. Assuming additive toxicity forPAH, the sum of TUs was calculated to predict the total toxicity of PAH mixtures in sediments. The EPAH model was developed from 10- and 14-d H. azteca water-only LC50 values. To obtain estimates of LC50 values for a wide range of PAHs, a quantitative structure-activity relationship (QSAR) model (log LC50 -log Kow) with a constant slope was derived using the time-variable LC50 values for four PAH congeners. The logistic-regression model was derived to assess the concentration-response relationship for field sediments, which showed that 1.3 (0.6-3.9) TU were required for a 50% probability that a sediment was toxic. The logistic-regression model reflects both the effects of co-occurring contaminants (i.e., nonmeasured PAH and unknown pollutants) and the overestimation of exposure to sediment-associated PAH.An apparent site-specific bioavailability limitation of sediment-associated PAH was found for a site contaminated by creosote. At this site, no toxic samples were less than 3.9 TU. Finally, the predictability of the EPAH model can be affected by species-specific responses (Hyalella vs Rhepoxynius); chemical specific (PAH vs DDT in H. azteca) bias [ABSTRACT FROM AUTHOR]